Arabic Metaphor Corpus (AMC) with Semantic and Sentiment Annotation - Lancaster University research directory Skip to main navigation Skip to search Skip to main content Lancaster University research directory Home Lancaster University research directory Logo Help & FAQ Home Profiles Research units Research output Projects Datasets Equipment Activities Press/Media Prizes Impacts Search by expertise, name or affiliation Arabic Metaphor Corpus (AMC) with Semantic and Sentiment Annotation Israa Alsiyat, Scott Piao, Mansour Almansour Data Science Institute Data Science Computing and Communications UCREL - University Centre for Computer Corpus Research on Language Research output: Contribution to...

In this paper, I discuss the testing of the Arabic Metaphor Corpus (AMC) [1] using newly designed automatic tools for sentiment classification for AMC based on semantic tags. The tool incorporates semantic emotional tags for sentiment classification. I evaluate the tool using standard methods, which are F-score, recall, and precision. The method is to show the impact of Arabic online metaphors on sentiment through the newly designed tools. To the best of our knowledge, this is the first approach to conduct sentiment classification for Arabic metaphors using semantic tags to find the impact of the metaphor.

Over the recent years, Arabic language resources and NLP tools have been under rapid development. One of the important tasks for Arabic natural language processing is the sentiment analysis. While a significant improvement has been achieved in this research area, the existing computational models and tools still suffer from the lack of capability of dealing with Arabic metaphorical expressions. Metaphor has an important role in Arabic language due to its unique history and culture. Metaphors provide a linguistic mechanism for expressing ideas and notions that can be different from their surface form. Therefore, in order to efficiently identify true sentiment of Arabic language data, a...